Simultaneous determination of wave velocity and thickness on overlapped signals using Forward Backward algorithm

Bustillo, Julien; Achdjian, Hossep; Arciniegas, Andres; Blanc, Laurianne

doi:10.1016/j.ndteint.2016.12.001

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…The ultrasonic method is based on measuring the transit time that is required for the ultrasonic pulse to go through the material several times. Hence, the knowledge about the precise physical characteristics of the material, such as the ultrasonic velocity, plays an important role [ 8 ] which can lead to inaccurate inspection results [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete

Hien

Kim

Hong

2022

Sensors

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In this study, we investigate a millimeter wave (mmWave) synthetic aperture radar (SAR) imaging scheme utilizing a low-cost frequency modulated continuous wave (FMCW) radar to take part in non-destructive testing which could be a useful tool for both civilian and military demands. The FMCW radar working in the frequency range from 76 GHz to 81 GHz is equipped with a 2-D moving platform aiming to reconstruct the 2-D image of the shape of the target object. Due to the lab environment containing several devices and furniture, various noise and interference signals from the floor are not avoidable. Therefore, the digital signal processing algorithms are joined to remove the undesired signals as well as improve the target recognition. This study adopts the range migration algorithms (RMAs) on the processed reflected signal data to form the image of the target because of its verified ability in this type of mission. On the other hand, the integration of compressed sensing (CS) algorithms into the SAR imaging system is also researched which helps to improve the performance of the system by reducing the measurement duration while still maintaining the image quality. Three minimization algorithms are used involving the imaging system as the CS solvers reconstruct the radar data before being processed by RMA to form the image. The proposed imaging scheme demonstrates its good ability with high azimuth resolution in the mission of detecting tiny cracks in the rebar of reinforced concrete. In addition, the participation of CS algorithms improves the performance of the scheme as the cracks on the rebar can be located on the images, which are reconstructed from only 30% of the dataset. The comparison of CS solvers shows that ADMM outperforms the other candidates in the reconstruction task.

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Section: Introductionmentioning

confidence: 99%

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete

Hien

Kim

Hong

2022

Sensors

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“…On the contrary, the ultrasonic method is widely employed for wall thickness inspections due to its advantages of [4,5]: being nonhazardous and applicable for thickness measurements of thick material, the detection of discontinuity, and the determination of material properties. However, the ultrasonic thickness measurement (UTM) is based on measuring the transit time that is required for a short ultrasonic pulse to travel through the material once, twice, or several 2 of 15 times, which leads to the need for the UTM system to know the precise physical characteristics, such as the ultrasonic velocity, going through the material [6]. Therefore, often authors investigate and develop new methods for determining ultrasonic velocity [7,8], but usually they present measurement results without the associated measurement uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

Hien

Hong

2021

Applied Sciences

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Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.

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“…Ultrasonic velocity is an important acoustic parameter in material characterization and as an impact factor is a subject of many authors research. Consequently, numerous studies have been conducted with the scope to investigate the changes of ultrasonic velocity in a variety of materials, and thus showed how the changes in the microstructure reflect the differences in propagation speed of the ultrasonic pulses [11][12][13]. The changes in the microstructure reflect the differences in propagation speed of the ultrasonic pulses what was the scope of investigation of Gur et al [14].…”

Section: Introductionmentioning

confidence: 99%

Coating Influence on Ultrasonic Thickness Measurement Result

Mihaljević¹,

Cajner²,

Markučić³

et al. 2018

Proceedings of the 29th International DAAAM Symposium 2018

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In this paper the Monte Carlo Simulation is presented and applied as a method for quantification of the coating influence on ultrasonic thickness measurement (UTM) results. UTM as a Non Destructive Testing (NDT) technique is commonly used for integrity assessment of components and the whole structures. It belongs to a field of measurement activities since the measurement result is shown quantitatively. Generally, a measurement result is complete only when it is followed up by a statement of the associated uncertainty. In order to evaluate the measurement uncertainty of ultrasonic thickness measurements all significant factors and their interactions should be taken into account. One of the influence factor is coating over the base material. In some cases the coating can be removed before the beginning of measurement procedure, but in some cases the measurement should be conducted through the coating. In this paper the coating influence on ultrasonic thickness measurement result was analysed and quantified by measuring the thickness of base material over the coating in repeatability conditions. In order to investigate the coating thickness impact of measurement component, measurements were conducted on prepared test specimen of steel. Extended measurement uncertainty of selected ultrasonic thickness measurement system was estimated.

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Simultaneous determination of wave velocity and thickness on overlapped signals using Forward Backward algorithm

Cited by 13 publications

References 22 publications

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete

Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

Coating Influence on Ultrasonic Thickness Measurement Result

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